MATEC Web Conf.
Volume 309, 20202019 International Conference on Computer Science Communication and Network Security (CSCNS2019)
|Number of page(s)||6|
|Section||Smart Algorithms and Recognition|
|Published online||04 March 2020|
Support vector machine filtering data aid on fatigue driving detection
1 Chongqing University of Science and Technology, 20 East Daxuecheng Road, Shapingba District, Chongqing China
2 Unitec Institute of Technology Private Bag , 92025, Victoria Street West Auckland New Zealand
* Corresponding author: email@example.com
This paper proposes an assumption that filtering out the confusing “awake” data from fatigue driving detection model promotes the accuracy of detection of “drowsy” status under real driving situation. Instead of focus on both “drowsy” and “awake” driving status, we set our first priority to alarm “drowsy” and temporarily ignore the accuracy of “awake” status recognition. The Support Vector Machine as a good classifier is employed for data filtering, provides more efficient training data and removes the data that may confuse the detection model. The results prove our assumption by 72% accuracy on “drowsy” recognition, which is higher than 38% recognition performed by detection without SVM filtering. In addition, the size of training samples after filtering for conducting detection model is extremely smaller than no filtering.
Key words: Support vector machine / Fatigue driving detection / Filtering data
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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